Stranding and scoping analysis for autonomous vehicle services

ABSTRACT

Aspects of the disclosure provide for identifying problematic areas within a service area for an autonomous vehicle transportation service. For instance, a starting location within the service area corresponding to a potential pickup location for passengers or cargo for the service may be identified. A destination within the service area may be identified. A simulation may be run in order to determine a route for a simulated vehicle to travel between the starting location and the destination. That the route includes a particular type of maneuver may be determined. A new simulation without allowing the simulated vehicle to complete the particular type of maneuver may be run. Whether the simulated vehicle reaches the destination in the new simulation may be determined. Based on the determination of whether the simulated vehicle reaches the destination in the new simulation, the starting location and destination location may be flagged as potentially problematic areas.

BACKGROUND

Autonomous vehicles, such as vehicles that do not require a humandriver, can be used to aid in the transport of passengers or items fromone location to another. Such vehicles may operate in a fully autonomousmode where users may provide some initial input, such as a pickup ordestination location, and the vehicle maneuvers itself to that location.When a person (or user) wants to be physically transported and/or totransport goods between two locations via a vehicle, they may use anynumber of taxi or delivery services. To date, these services typicallyinvolve a human driver who is given dispatch instructions to a locationto pick up and drop off the user and/or goods. In many instances, humandrivers may tend to take the fastest or most direct route to thedestination location. However, in the case of autonomous vehicles, wheresome areas may be “off-limits” for one reason or another, this may notalways be possible and may sometimes result in strandings or rather,situations in which the vehicle is not able to reach its destination orsome other location.

BRIEF SUMMARY

One aspect of the disclosure provides a method of identifyingproblematic areas within a service area for an autonomous vehicletransportation service. The method includes identifying, by one or moreprocessors, a starting location within the service area corresponding toa potential pickup location for passengers or cargo for the service;identifying, by the one or more processors, a destination locationwithin the service area for the starting location; running, by the oneor more processors, a simulation to determine a route for a simulatedvehicle to travel between the starting location and the destinationlocation; determining, by the one or more processors, that thedetermined route includes a particular type of maneuver; running, by theone or more processors, a new simulation without allowing the simulatedvehicle to complete the particular type of maneuver; determining, by theone or more processors, whether the simulated vehicle reaches thedestination location in the new simulation; and based on thedetermination of whether the simulated vehicle reaches the destinationlocation in the new simulation, flagging, by the one or more processors,at least one of the starting location or the destination location aspotentially problematic areas.

In one example, the method also includes running a set of simulationsfor the starting location by identifying a plurality of destinationlocations for the starting location and determining routes between thestarting location and each of the plurality of destination locations;identifying ones of the determined routes that include the particulartype of maneuver; running a set of new simulations without allowing thesimulated vehicle to complete the particular type of maneuver; anddetermining whether the simulated vehicle reaches the destinationlocation in each of the set of new simulations, and wherein flagging isfurther based on the determination of whether the simulated vehiclereaches the destination location in each of the set of new simulations.In this example, flagging the starting location is further based on acomparison a number of new simulations where the simulated vehicle doesnot the destination location to a threshold value. In another example,the service area includes a boundary across which the simulated vehicleis unable to cross, and wherein determining the simulated vehiclereaches the destination location in the new simulation is based onwhether the simulated vehicle becomes stranded by the boundary. Inanother example, the starting location is identified by randomlyselecting a location within the service area. In another example, thestarting location is identified from a plurality of predetermined pickupand drop off locations within the service area. In this example, thestarting location is identified from the plurality of predeterminedpickup and drop off locations randomly. Alternatively, the startinglocation is identified from the plurality of predetermined pickup anddrop off locations in order to test a specific area within the servicearea. In another example, the destination location is identified from aplurality of predetermined pickup and drop off locations within theservice area. In another example, the simulation is run using a routingsystem software stack for an autonomous vehicle to determine thedetermined route. In another example, the particular type of maneuver ischanging lanes. In another example, the method also includes flagging aplurality of problematic areas and drawing a polygon around theplurality of problematic areas and the starting location.

Another aspect of the disclosure provides a system for identifyingproblematic areas within a service area for an autonomous vehicletransportation service. The system includes one or more server computingdevices having one or more processors configured to: identify a startinglocation within the service area corresponding to a potential pickuplocation for passengers or cargo for the service; identify a destinationlocation within the service area for the starting location; run asimulation to determine a route for a simulated vehicle to travelbetween the starting location and the destination location; determinethat the determined route includes a particular type of maneuver; run anew simulation without allowing the simulated vehicle to complete theparticular type of maneuver; determine whether the simulated vehiclereaches the destination location in the new simulation; and based on thedetermination of whether the simulated vehicle reaches the destinationlocation in the new simulation, flag at least one of the startinglocation or destination location as potentially problematic areas.

In one example, the one or more processors are further configured to:run a set of simulations for the starting location by identifying aplurality of destination locations for the starting location anddetermining routes between the starting location and each of theplurality of destination locations; identify ones of the determinedroutes that include the particular type of maneuver; run a set of newsimulations without allowing the simulated vehicle to complete theparticular type of maneuver; and determine whether the simulated vehiclereaches the destination location in each of the set of new simulations,and wherein flagging is further based on the determination of whetherthe simulated vehicle reaches the destination location in each of theset of new simulations. In another example, the service area includes aboundary across which the simulated vehicle is unable to cross, andwherein determining the simulated vehicle reaches the destinationlocation in the new simulation is based on whether the simulated vehiclebecomes stranded by the boundary. In another example, the startinglocation is identified by randomly selecting a location within theservice area. In another example, the starting location is identifiedfrom a plurality of predetermined pickup and drop off locations withinthe service area. In another example, the destination location isidentified from a plurality of predetermined pickup and drop offlocations within the service area. In another example, the simulation isrun using a routing system software stack for an autonomous vehicle todetermine the determined route. In another example, the one or moreprocessors are further configured to: flag a plurality of problematicareas and draw a polygon around the plurality of problematic areas andthe starting location.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram of an example vehicle in accordance withan exemplary embodiment.

FIGS. 2A, 2B and 2C are an example of map information in accordance withaspects of the disclosure.

FIG. 3 is an example external view of a vehicle in accordance withaspects of the disclosure.

FIG. 4 is a pictorial diagram of an example system in accordance withaspects of the disclosure.

FIG. 5 is a functional diagram of the system of FIG. 4 in accordancewith aspects of the disclosure.

FIG. 6 is an example flow diagram in accordance with aspects of thedisclosure.

FIG. 7 is an example of map information and a route in accordance withaspects of the disclosure.

FIG. 8 is an example of map information and a route in accordance withaspects of the disclosure.

FIG. 9 is an example of map information, problematic locations, and apolygon in accordance with aspects of the disclosure.

DETAILED DESCRIPTION Overview

The technology relates to assessing the viability of service areas forautonomous vehicles. This may allow for the identification of potentialstranding situations where a vehicle is unable to reach its destination.For instance, certain maneuvers, such as lane changes in order to maketurns, can be difficult for autonomous vehicles which may configured toavoid unsafe situations which human drivers may be willing to try. Inaddition, autonomous vehicles may be subject to geographicalrestrictions, for instance vehicles may need to remain within a servicearea, while human drivers typically would not be subject to suchrestrictions. Because of this, in some instances, an autonomous vehiclemay become stranded because such vehicles may be programmed to avoidunsafe maneuvers and may no longer be able to make progress towards adestination due to geographical restrictions. If the vehicle istransporting cargo or passengers, this can lead to other issues. Again,in order to reduce such situations, a service area may be “tested” byrunning simulations to identify problematic areas.

In order to run the aforementioned simulations, a plurality of pickupand drop off locations may be identified. Each pickup location mayrepresent a location for picking up passengers and/or cargo, and eachdrop off location may represent a destination or location for droppingoff passengers and/or cargo. A plurality of starting locations for thesimulations may be selected from the pickup and/or drop off locations.For each selected starting location, a plurality of destinationlocations may be selected from the plurality of pickup and/or drop offlocations.

Each simulation may then be “run” by determining a route for each a pairof starting and destination locations. This may involve inputting eachpair of starting and destination locations into a software stack of arouting system used by the autonomous vehicles. The routing system maydetermine an overall route between each starting location anddestination location. Each of these routes may include particular typesof maneuvers.

A subset of the simulations that include a particular type of maneuvermay be identified. As an example, particular types of maneuvers mayinclude, right and left turns, unprotected left turns, lane changes,multi-point turns, etc. For the subset of simulations, a plurality ofnew simulations may be run. Each of these new simulations may be runbased on an assumption that the vehicle will be unable to complete themaneuver and will have to re-route itself to the destination of thatsimulation.

Any of the new simulations that do not reach the destination locationmay be identified. For each of the identified new simulations, the(original) selected starting location may be flagged as unable to reachthe destination location. This information may help engineers visualizethe practicality of a given service area.

The features described herein may be useful in assessing the viabilityof service areas or software-restricted maneuvers for autonomousvehicles. This may allow for the identification of potential strandingsituations where a vehicle is unable to reach its destination, or wherea vehicle is unlikely to reach its destination, and in turn, may be usedto “carve-out” these areas. As a result, the number of strandings may bedramatically reduced and the passenger or user experience may beimproved.

Example Systems

As shown in FIG. 1, a vehicle 100 in accordance with one aspect of thedisclosure includes various components. While certain aspects of thedisclosure are particularly useful in connection with specific types ofvehicles, the vehicle may be any type of vehicle including, but notlimited to, cars, trucks, motorcycles, buses, recreational vehicles,etc. The vehicle may have one or more computing devices, such ascomputing devices 110 containing one or more processors 120, memory 130and other components typically present in general purpose computingdevices.

The memory 130 stores information accessible by the one or moreprocessors 120, including instructions 132 and data 134 that may beexecuted or otherwise used by the processor 120. The memory 130 may beof any type capable of storing information accessible by the processor,including a computing device-readable medium, or other medium thatstores data that may be read with the aid of an electronic device, suchas a hard-drive, memory card, ROM, RAM, DVD or other optical disks, aswell as other write-capable and read-only memories. Systems and methodsmay include different combinations of the foregoing, whereby differentportions of the instructions and data are stored on different types ofmedia.

The instructions 132 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computingdevice code on the computing device-readable medium. In that regard, theterms “software,” “instructions” and “programs” may be usedinterchangeably herein. The instructions may be stored in object codeformat for direct processing by the processor, or in any other computingdevice language including scripts or collections of independent sourcecode modules that are interpreted on demand or compiled in advance.Functions, methods and routines of the instructions are explained inmore detail below.

The data 134 may be retrieved, stored or modified by processor 120 inaccordance with the instructions 132. For instance, although the claimedsubject matter is not limited by any particular data structure, the datamay be stored in computing device registers, in a relational database asa table having a plurality of different fields and records, XMLdocuments or flat files. The data may also be formatted in any computingdevice-readable format.

The one or more processors 120 may be any conventional processors, suchas commercially available CPUs. Alternatively, the one or moreprocessors may be a dedicated device such as an ASIC or otherhardware-based processor. Although FIG. 1 functionally illustrates theprocessor, memory, and other elements of computing devices 110 as beingwithin the same block, it will be understood by those of ordinary skillin the art that the processor, computing device, or memory may actuallyinclude multiple processors, computing devices, or memories that may ormay not be stored within the same physical housing. For example, memorymay be a hard drive or other storage media located in a housingdifferent from that of computing devices 110. Accordingly, references toa processor or computing device will be understood to include referencesto a collection of processors or computing devices or memories that mayor may not operate in parallel.

Computing devices 110 may include all of the components normally used inconnection with a computing device such as the processor and memorydescribed above as well as a user input 150 (e.g., a mouse, keyboard,touch screen and/or microphone) and various electronic displays (e.g., amonitor having a screen or any other electrical device that is operableto display information). In this example, the vehicle includes aninternal electronic display 152 as well as one or more speakers 154 toprovide information or audio-visual experiences. In this regard,internal electronic display 152 may be located within a cabin of vehicle100 and may be used by computing devices 110 to provide information topassengers within the vehicle 100.

Computing devices 110 may also include one or more wireless networkconnections 156 to facilitate communication with other computingdevices, such as the client computing devices and server computingdevices described in detail below. The wireless network connections mayinclude short range communication protocols such as Bluetooth, Bluetoothlow energy (LE), cellular connections, as well as various configurationsand protocols including the Internet, World Wide Web, intranets, virtualprivate networks, wide area networks, local networks, private networksusing communication protocols proprietary to one or more companies,Ethernet, WiFi and HTTP, and various combinations of the foregoing.

In one example, computing devices 110 may be control computing devicesof an autonomous driving computing system or incorporated into vehicle100. The autonomous driving computing system may be capable ofcommunicating with various components of the vehicle in order to controlthe movement of vehicle 100 according to the autonomous vehicle controlsoftware of memory 130 as discussed further below. For example,returning to FIG. 1, computing devices 110 may be in communication withvarious systems of vehicle 100, such as deceleration system 160,acceleration system 162, steering system 164, signaling system 166,planning system 168, routing system 170, positioning system 172,perception system 174, and power system 176 (i.e. the vehicle's engineor motor) in order to control the movement, speed, etc. of vehicle 100in accordance with the instructions 132 of memory 130. Each of thesesystems may include various hardware (processors and memory similar toprocessors 120 and memory 130) as well as software, in order to enablethese systems to perform various tasks. Again, although these systemsare shown as external to computing devices 110, in actuality, thesesystems may also be incorporated into computing devices 110, again as anautonomous driving computing system for controlling vehicle 100.

As an example, computing devices 110 may interact with one or moreactuators of the deceleration system 160 and/or acceleration system 162,such as brakes, accelerator pedal, and/or the engine or motor of thevehicle, in order to control the speed of the vehicle. Similarly, one ormore actuators of the steering system 164, such as a steering wheel,steering shaft, and/or pinion and rack in a rack and pinion system, maybe used by computing devices 110 in order to control the direction ofvehicle 100. For example, if vehicle 100 is configured for use on aroad, such as a car or truck, the steering system may include one ormore actuators to control the angle of wheels to turn the vehicle.Signaling system 166 may be used by computing devices 110 in order tosignal the vehicle's intent to other drivers or vehicles, for example,by lighting turn signals or brake lights when needed.

Planning system 168 may be used by computing devices 110 in order todetermine and follow a route generated by a routing system 170 to alocation. For instance, the routing system 170 may use map informationto determine a route from a current location of the vehicle to adestination location. The planning system 172 may periodically generatetrajectories, or short-term plans for controlling the vehicle for someperiod of time into the future, in order to follow the route to thedestination. In this regard, the planning system 168, routing system170, and/or data 134 may store detailed map information, e.g., highlydetailed maps identifying the shape and elevation of roadways, lanelines, intersections, crosswalks, speed limits, traffic signals,buildings, signs, real time traffic information, vegetation, or othersuch objects and information.

FIGS. 2A, 2B, and 2C are an example of map information 200 for a sectionof roadway corresponding to a service area for autonomous vehicles suchas vehicle 100. Turning to FIG. 2A, the map information 200 includesinformation identifying the shape, location, and other characteristicsof various features including lane lines represented by dashed-lines210, 212, 214 and solid lines 216, 218. These lane lines may designateroads or otherwise drivable areas such as lanes 220, 222, 224, 226 andcul-de-sac 230. The map information also identifies intersections 240,242, 244, non-drivable areas (such as buildings, parks, etc.)represented by shaded areas 250, 252, 254, as well as other featuressuch as train tracks 260. Although only a few features are shown andidentified, the map information 200 may be highly-detailed and includevarious additional features, including for instance the locations andboundaries of blockages as discussed further below.

As shown in FIG. 2B, the map information may also identify a boundary270 for a service area. As shown, the boundary is a rectangular shape,but the boundary may be drawn as a polygon or any other shape and may bedefined by physical or other types barrier or restrictions on thevehicles. This boundary may be fixed, for instance, because the vehiclesmay rely on maps and the boundary may differentiate between mapped andunmapped areas. The boundary may also be based on other restrictions forthe vehicles, such as not crossing railroad tracks, not crossing throughparking lots, not entering circles (or roundabouts), not making U-turns,etc. These restrictions may be encoded into the map information and/oran autonomous vehicle's routing system software module. For instance,map information can encode that one particular lane is problematic (e.g.there is a specific turn that the vehicle 100 is unable to accomplish),and the routing system can determine that some roads are problematicbased on the contents of the map information (e.g. freeways have speedlimits>45, so router disallows it) As an example, boundary 270 isdefined in part by railroad tracks 260. In addition, in this example,the service area does not cover all of the map information 200, thoughin some instances, the extents of the map information may define theboundary.

Turning to FIG. 2C, the map information 200 may include a plurality ofpredetermined pickup and drop off locations A, B, C, D, E, F within theboundary 270 (not shown). For instance, each pickup location mayrepresent a location for picking up passengers and/or cargo, and eachdrop off location may represent a destination or location for droppingoff passengers and/or cargo. In many cases, a pickup location may alsobe a drop off location, and vice versa. In this regard, each oflocations A, B, C, D, E, and F may represent either a pickup or drop offlocation or both. These pickup and/or drop off locations may be handselected or automatically identified using a plurality of heuristics.For instance, pickup and/or drop off locations may only be on low speedroads (e.g. under 35 miles per hour), not in no stopping or standingzones (e.g. not in front of a fire hydrant), not in certain areas (e.g.on an on or off ramp for a highway), etc. In this regard, each of thepickup and/or drop off locations must be feasible, that is a reasonablelocation to pick up and drop off passengers, as well as “reachable”,that is, part of the service area that is mapped.

Although the map information is depicted herein as an image-based map,the map information need not be entirely image based (for example,raster). For example, the map information may include one or more roadgraphs or graph networks of information such as roads, lanes,intersections, and the connections between these features. Each featuremay be stored as graph data and may be associated with information suchas a geographic location and whether or not it is linked to otherrelated features, for example, a stop sign may be linked to a road andan intersection, etc. In some examples, the associated data may includegrid-based indices of a road graph to allow for efficient lookup ofcertain road graph features.

Positioning system 172 may be used by computing devices 110 in order todetermine the vehicle's relative or absolute position on a map or on theearth. For example, the position system 172 may include a GPS receiverto determine the device's latitude, longitude and/or altitude position.Other location systems such as laser-based localization systems,inertial-aided GPS, or camera-based localization may also be used toidentify the location of the vehicle. The location of the vehicle mayinclude an absolute geographical location, such as latitude, longitude,and altitude as well as relative location information, such as locationrelative to other cars immediately around it which can often bedetermined with less noise that absolute geographical location.

The positioning system 172 may also include other devices incommunication with computing devices 110, such as an accelerometer,gyroscope or another direction/speed detection device to determine thedirection and speed of the vehicle or changes thereto. By way of exampleonly, an acceleration device may determine its pitch, yaw or roll (orchanges thereto) relative to the direction of gravity or a planeperpendicular thereto. The device may also track increases or decreasesin speed and the direction of such changes. The device's provision oflocation and orientation data as set forth herein may be providedautomatically to the computing devices 110, other computing devices andcombinations of the foregoing.

The perception system 174 also includes one or more components fordetecting objects external to the vehicle such as other vehicles,obstacles in the roadway, traffic signals, signs, trees, etc. Forexample, the perception system 174 may include lasers, sonar, radar,cameras and/or any other detection devices that record data which may beprocessed by computing device 110. In the case where the vehicle is apassenger vehicle such as a minivan, the minivan may include a laser orother sensors mounted on the roof or other convenient location. Forinstance, FIG. 3 is an example external view of vehicle 100. In thisexample, roof-top housing 310 and dome housing 312 may include a LIDARsensor as well as various cameras and radar units. In addition, housing320 located at the front end of vehicle 100 and housings 330, 332 on thedriver's and passenger's sides of the vehicle may each store a LIDARsensor. For example, housing 330 is located in front of driver door 350.Vehicle 100 also includes housings 340, 342 for radar units and/orcameras also located on the roof of vehicle 100. Additional radar unitsand cameras (not shown) may be located at the front and rear ends ofvehicle 100 and/or on other positions along the roof or roof-top housing310. Vehicle 100 also includes many features of a typical passengervehicle such as doors 350, 352, wheels 360, 362, etc.

The various systems of the vehicle may function using autonomous vehiclecontrol software in order to determine how to and to control thevehicle. As an example, a perception system software module of theperception system 174 may use sensor data generated by one or moresensors of an autonomous vehicle, such as cameras, LIDAR sensors, radarunits, sonar units, etc., to detect and identify objects and theircharacteristics. These characteristics may include location, type,heading, orientation, speed, acceleration, change in acceleration, size,shape, etc. In some instances, characteristics may be input into abehavior prediction system software module which uses various modelsbased on object type to output a predicted future behavior for adetected object. In other instances, the characteristics may be put intoone or more detection system software modules, such as a constructionzone detection system software module configured to detect constructionzones from sensor data generated by the one or more sensors of thevehicle as well as an emergency vehicle detection system configured todetect emergency vehicles from sensor data generated by sensors of thevehicle. Each of these detection system software modules may usesvarious models to output a likelihood of a construction zone or anobject being an emergency vehicle. Detected objects, predicted futurebehaviors, various likelihoods from detection system software modules,the map information identifying the vehicle's environment, positioninformation from the positioning system 172 identifying the location andorientation of the vehicle, a destination for the vehicle as well asfeedback from various other systems of the vehicle (including a routegenerated by the routing system 170) may be input into a planning systemsoftware module of the planning system 168. The planning system may usethis input to generate trajectories for the vehicle to follow for somebrief period of time into the future. A control system software moduleof the computing devices 110 may be configured to control movement ofthe vehicle, for instance by controlling braking, acceleration andsteering of the vehicle, in order to follow a trajectory.

The computing devices 110 may control the direction and speed of thevehicle autonomously by controlling various components. In order to doso, computing devices 110 may cause the vehicle to accelerate (e.g., byincreasing fuel or other energy provided to the engine by accelerationsystem 162), decelerate (e.g., by decreasing the fuel supplied to theengine, changing gears, and/or by applying brakes by deceleration system160), change direction (e.g., by turning the front or rear wheels ofvehicle 100 by steering system 164), and signal such changes (e.g., bylighting turn signals of signaling system 166). Thus, the accelerationsystem 162 and deceleration system 160 may be a part of a drivetrainthat includes various components between an engine of the vehicle andthe wheels of the vehicle. Again, by controlling these systems,computing devices 110 may also control the drivetrain of the vehicle inorder to maneuver the vehicle autonomously.

Computing device 110 of vehicle 100 may also receive or transferinformation to and from other computing devices, such as those computingdevices that are a part of the transportation service as well as othercomputing devices. FIGS. 4 and 5 are pictorial and functional diagrams,respectively, of an example system 400 that includes a plurality ofcomputing devices 410, 420, 430, 440 and a storage system 450 connectedvia a network 460. System 400 also includes vehicle 100 and vehicles100A, which may be configured the same as or similarly to vehicle 100.Although only a few vehicles and computing devices are depicted forsimplicity, a typical system may include significantly more.

As shown in FIG. 5, each of computing devices 410, 420, 430, 440 mayinclude one or more processors, memory, data and instructions. Suchprocessors, memories, data and instructions may be configured similarlyto one or more processors 120, memory 130, data 134, and instructions132 of computing device 110.

The network 460, and intervening nodes, may include variousconfigurations and protocols including short range communicationprotocols such as Bluetooth, Bluetooth LE, the Internet, World Wide Web,intranets, virtual private networks, wide area networks, local networks,private networks using communication protocols proprietary to one ormore companies, Ethernet, WiFi and HTTP, and various combinations of theforegoing. Such communication may be facilitated by any device capableof transmitting data to and from other computing devices, such as modemsand wireless interfaces.

In one example, one or more computing devices 410 may include one ormore server computing devices having a plurality of computing devices,e.g., a load balanced server farm, that exchange information withdifferent nodes of a network for the purpose of receiving, processingand transmitting the data to and from other computing devices. Forinstance, one or more computing devices 410 may include one or moreserver computing devices that are capable of communicating withcomputing device 110 of vehicle 100 or a similar computing device ofvehicle 100A as well as computing devices 420, 430, 440 via the network460. For example, vehicles 100, 100A, may be a part of a fleet ofvehicles that can be dispatched by server computing devices to variouslocations. In this regard, the server computing devices 410 may functionas a dispatching server computing system which can be used to dispatchvehicles such as vehicle 100 and vehicle 100A to different locations inorder to pick up and drop off passengers. In addition, server computingdevices 410 may use network 460 to transmit and present information to auser, such as user 422, 432, 442 on a display, such as displays 424,434, 444 of computing devices 420, 430, 440. In this regard, computingdevices 420, 430, 440 may be considered client computing devices.

As shown in FIG. 5, each client computing device 420, 430, 440 may be apersonal computing device intended for use by a user 422, 432, 442, andhave all of the components normally used in connection with a personalcomputing device including a one or more processors (e.g., a centralprocessing unit (CPU)), memory (e.g., RAM and internal hard drives)storing data and instructions, a display such as displays 424, 434, 444(e.g., a monitor having a screen, a touch-screen, a projector, atelevision, or other device that is operable to display information),and user input devices 426, 436, 446 (e.g., a mouse, keyboard,touchscreen or microphone). The client computing devices may alsoinclude a camera for recording video streams, speakers, a networkinterface device, and all of the components used for connecting theseelements to one another.

Although the client computing devices 420, 430, and 440 may eachcomprise a full-sized personal computing device, they may alternativelycomprise mobile computing devices capable of wirelessly exchanging datawith a server over a network such as the Internet. By way of exampleonly, client computing device 420 may be a mobile phone or a device suchas a wireless-enabled PDA, a tablet PC, a wearable computing device orsystem, or a netbook that is capable of obtaining information via theInternet or other networks. In another example, client computing device430 may be a wearable computing system, shown as a wristwatch as shownin FIG. 4. As an example, the user may input information using a smallkeyboard, a keypad, microphone, using visual signals with a camera, or atouch screen.

As with memory 130, storage system 450 can be of any type ofcomputerized storage capable of storing information accessible by theserver computing devices 410, such as a hard-drive, memory card, ROM,RAM, DVD, CD-ROM, write-capable, and read-only memories. In addition,storage system 450 may include a distributed storage system where datais stored on a plurality of different storage devices which may bephysically located at the same or different geographic locations.Storage system 450 may be connected to the computing devices via thenetwork 460 as shown in FIGS. 4 and 5, and/or may be directly connectedto or incorporated into any of the computing devices 110, 410, 420, 430,440, etc.

Storage system 450 may store various types of information as describedin more detail below. This information may be retrieved or otherwiseaccessed by a server computing device, such as one or more servercomputing devices 410, in order to perform some or all of the featuresdescribed herein. For instance, storage system 450 may store a versionof map information 200 including boundary 270. Of course, as thisboundary and/or the map information is updated, the version in thestorage system 450 may also be updated.

The storage system 450 may also store a software stack or module for theaforementioned routing system 170 as well as historical tripinformation. The software module may be programmed in order to determinea route between two locations given the map information. In someinstances, the software modules may be programmed to restrict certaintypes of maneuvers when determining a route, such as changing lanes intoo short a distance, making a u-turn, etc.

Example Methods

In addition to the operations described above and illustrated in thefigures, various operations will now be described. It should beunderstood that the following operations do not have to be performed inthe precise order described below. Rather, various steps can be handledin a different order or simultaneously, and steps may also be added oromitted.

The technology relates to assessing the viability of service areas forautonomous vehicles. This may allow for the identification of potentialstranding situations where a vehicle is unable to reach its destination.For instance, certain maneuvers, such as lane changes in order to maketurns, can be difficult for autonomous vehicles which may configured toavoid risky or dangerous maneuvers which human drivers may be willing totry. As a result, an autonomous vehicle may become stranded. If thevehicle is transporting cargo or passengers, this can lead to otherissues. Again, in order to reduce such situations, a service area may be“tested” by running simulations to identify problematic areas.

FIG. 6 includes an example flow diagram 600 of some of the examples foridentifying problematic areas within a service area, such as thatdefined by boundary 270, for an autonomous vehicle transportationservice, which may be performed by one or more processors such as theprocessors of computing devices 410. For instance, at block 610, astarting location within the service area corresponding to a potentialpickup location for passengers or cargo for the service may beidentified. As an example, the starting location may be a pickup and/ora drop off location, such as any of locations A, B, C, D, E, and F ofFIG. 2C.

A plurality of starting locations for the simulations may be selectedfrom the pickup and/or drop off locations. For example, for a 10 squaremile area, 10,000 of the pickup locations may be selected as startinglocations. This selection may be random, or may focus on specific areasto be tested, such as a particular neighborhood, a newly mapped area, anarea for which strandings have occurred previously, etc.

For each selected starting location, a plurality of destinationlocations may be selected from the plurality of pickup/drop offlocations. For example, returning to FIG. 6, at block 620, a destinationlocation within the service area is identified for the startinglocation. Returning to the example above, for each of the 10,000selected pickup locations, 50 drop off locations may be selected asdestination locations. In this regard, using the example numbers above,the total number of pairs of selected starting and destination locationsfor the simulations may be 500,000. Again, this selection may be random,or may focus on specific areas to be tested, such as a particularneighborhood, a newly mapped area, an area for which strandings haveoccurred previously, etc. For instance, location A may be selected as apickup location, and may be paired with each of locations B, C, D, E,and F, or any subset therein, as drop off locations.

Each simulation may then be “run” by determining a route for each a pairof starting and destination locations. For instance, as shown in block630 of FIG. 6, a simulation may be run in order to determine a route fora simulated vehicle to travel between the starting location and thedestination location. As an example, a simulation may be run for each ofthe aforementioned 500,000 pairs or for A to B, A to C, A to D, A to E,and A to F. This may involve inputting each pair of starting anddestination locations into the software stack of the routing system 170,used by the autonomous vehicles, such as that stored in storage system450. The routing system may determine an overall route between eachstarting location and destination location. For instance, FIG. 7,depicts a route 710 between location A and location B.

Each of these routes may include particular types of maneuvers. Theseparticular types of maneuvers may include, right and left turns,unprotected left turns, lane changes, multi-point turns, etc. Forinstance, route 710 includes a simulated (or actual) vehicle making anunprotected left turn out of the cul-de-sac into lane 220, a protectedright turn into lane 222 at intersection 240, a lane change between lane222 and lane 224, a protected left at intersection 242 into lane 224(see FIG. 2A), and an unprotected left turn at intersection 244 intolane 228.

A subset of the simulations that include a particular type of maneuvermay be identified. As shown in block 640 of FIG. 6, that the determinedroute includes a particular type of maneuver is determined. Forinstance, simulations, such as the simulation that generated route 710,that include lane changes or unprotected turns may be included in thesubset. This subset may include all simulations having such maneuvers(e.g. all lane changes or all unprotected turns), or only a portion ofthose simulations which an autonomous vehicle is unlikely to be able tocomplete the maneuver. As an example, a model of lane change successrate may be used to select particular simulations having lane changesthat a vehicle is unlikely to be able to complete. This model mayidentify lane changes that must occur within a certain distance (e.g.less than a half of a mile or more or less) or a certain amount of time(e.g. 60 seconds or more or less). In this regard, the model may mightuse features including the length of a lane change opportunity, time ofday, etc. For example, model might estimate that the longer the lanechange opportunity, the more likely it is for the lane change to besuccessful. The model itself may be a machine learned model trainedusing past successful and unsuccessful lane changes. Alternatively, themodel could be a hand-tuned model.

For the subset of simulations, a plurality of new simulations may berun. For instance, as shown in block 650 of FIG. 6, a new simulation isrun without allowing the simulated vehicle to complete the particulartype of maneuver. Each of these new simulations may be run based on anassumption that the vehicle will be unable to complete the maneuver andwill have to re-route itself to the destination of that simulation. Forinstance, for a starting and destination location pair of the subset,those starting and destination locations may remain fixed and a newsimulation may be run by rerouting a simulated vehicle when thesimulated vehicle has passed the location of the maneuver.

For example, a new simulation may be run that prohibits the routingsystem from allowing the lane change between lanes 222 and 224 alongroute 710. Turning to FIG. 8, at the point of the lane change in theroute 710, the routing system would have to rely on an assumption thatthe lane change is not possible, and therefore re-route the vehicle toreach the location B. In this regard, the routing system may generate anew route 810. Up until location 820 on route 810, route 810 is the sameas route 710. However, after location 820, the route does not continueto location B. In this regard, a simulated vehicle, for instancecorresponding to vehicle 100, would be stopped at intersection 242 andbecomes stranded at location 830. In other words, boundary 270 wouldprevent the simulated vehicle from crossing the railroad tracks 260 ormaking any further progress towards the location B. If anything, thesimulated vehicle could make a right turn at intersection 242, but thevehicle would still be unable to reach or even make progress towards thelocation B.

Alternatively, the new simulations may be run by using a new startinglocation past the location of the maneuver and the destination location.In this regard, the destination location may be the original destinationor rather, be unchanged from the original simulation. In other words,the route would start at location 820 (or some other location beyondlocation 820, such as further down lane 222 towards intersection 242)rather than at location A.

Any of the new simulations which do not result in the vehicle reachingthe destination location may be identified. Returning to FIG. 6, whetherthe simulated vehicle reaches the destination location in the newsimulation is determined at block 660. This may occur, for instance, ifthe simulated vehicle is unable to avoid leaving the service area (forinstance, crossing a boundary as in the example of route 810 of FIG. 8)or making some other impermissible maneuver (for instance, making aU-turn).

For each of the identified new simulations, the (original) selectedstarting location may be flagged as unable to reach the destinationlocation and the destination location may be flagged as beingunreachable from the (original) selected starting location. Forinstance, at block 670, based on the determination of whether thesimulated vehicle reaches the destination location in the newsimulation, at least one of the starting location or the destinationlocation is flagged potentially problematic areas. For example, theseselected starting locations, such as location A, may be identified as“pins” on a map or may be used to generate a heat map of areas that aremore or less likely to cause strandings or rather, locations where thevehicles are unable to make progress towards the destination or someother location. This information may help engineers visualize thepracticality of a given service area.

In some instances, the number of new simulations that do not reach thedestination for a particular selected starting location may be fairlyhigh. In such cases, it may be useful to “carve-out” the areas of suchselected starting locations or rather, prevent an autonomous vehiclefrom being able to pick up passengers or cargo within such areas. Inorder to do so, for each selected starting location, the number of newsimulations (if any) that do not reach one of the destination locationsfor that selected starting location, may be compared to a thresholdvalue. If the threshold value is or is not met, or rather if there aretoo many failures, the selected starting location and/or the destinationlocation may be identified a problematic pickup location. For instance,if 50% (or more or less) of the new simulations for a selected startinglocation do not reach the destination location, this may indicate thatthe selected starting location is a problematic pickup location and thatthe destination location is also a problematic drop off location.

A plurality of problematic pickup locations may be grouped together, forinstance, by drawing a polygon around the problematic pickup locations.This drawing may be performed manually, or may be automated, bycomputing the smallest or other type of area that encompasses all of theproblematic pickup locations within some distance of one another. Forinstance, FIG. 9 may include a plurality of pickup location that havebeen identified as problematic including location A and locations P. Apolygon 900 has been drawn around these points, for instance, manuallyin order to “isolate” a problematic region of the map information forpickup and/or drop off locations. As another instance, the map may bedivided into sections, such as fixed-sized squares or other grid cells.Then, the largest connected set of squares without any problematicpickup locations may be the largest contiguous service area that can beused without problems. Regardless of the method used, any isolatedproblematic regions can be subtracted away from the service area, sothat vehicles are prevented from picking up passengers or cargo, orentering the problematic region for any reason. Therefore, since pickupsor dropoffs or either are prevented in these isolated areas, autonomousvehicles may be less likely to encounter a stranding event.

The aforementioned simulations may be re-run as needed. For instance,each time the service area is updated or problematic regions aredisallowed, the simulations may be re-run to determine the second-ordereffects of making those changes. Similarly, the simulations may bere-run each time the service area (or rather, if boundary 270), therouting system, and/or the map information is changed or updated. Forinstance, changes to the software stack of the routing system may resultin the increased ability of a vehicle to perform a particular maneuveror may completely change the route that the routing system generates.

The features described herein may be useful in assessing the viabilityof service areas or software-restricted maneuvers for autonomousvehicles. This may allow for the identification of potential strandingsituations where a vehicle is unable to reach its destination, or wherea vehicle is unlikely to reach its destination, and in turn, may be usedto “carve-out” these areas. As a result, the number of strandings may bedramatically reduced and the passenger or user experience may beimproved.

Unless otherwise stated, the foregoing alternative examples are notmutually exclusive, but may be implemented in various combinations toachieve unique advantages. As these and other variations andcombinations of the features discussed above can be utilized withoutdeparting from the subject matter defined by the claims, the foregoingdescription of the embodiments should be taken by way of illustrationrather than by way of limitation of the subject matter defined by theclaims. In addition, the provision of the examples described herein, aswell as clauses phrased as “such as,” “including” and the like, shouldnot be interpreted as limiting the subject matter of the claims to thespecific examples; rather, the examples are intended to illustrate onlyone of many possible embodiments. Further, the same reference numbers indifferent drawings can identify the same or similar elements.

1. A method of identifying problematic areas within a service area foran autonomous vehicle transportation service, the method comprising:identifying, by one or more processors, a starting location within theservice area corresponding to a potential pickup location for passengersor cargo for the service; identifying, by the one or more processors, adestination location within the service area for the starting location;running, by the one or more processors, a simulation to determine aroute for a simulated vehicle to travel between the starting locationand the destination location; determining, by the one or moreprocessors, that the determined route includes a particular type ofmaneuver; running, by the one or more processors, a new simulationwithout allowing the simulated vehicle to complete the particular typeof maneuver; determining, by the one or more processors, whether thesimulated vehicle reaches the destination location in the newsimulation; and based on the determination of whether the simulatedvehicle reaches the destination location in the new simulation,flagging, by the one or more processors, at least one of the startinglocation or the destination location as potentially problematic areas.2. The method of claim 1, further comprising: running a set ofsimulations for the starting location by identifying a plurality ofdestination locations for the starting location and determining routesbetween the starting location and each of the plurality of destinationlocations; identifying ones of the determined routes that include theparticular type of maneuver; running a set of new simulations withoutallowing the simulated vehicle to complete the particular type ofmaneuver; and determining whether the simulated vehicle reaches thedestination location in each of the set of new simulations, and whereinflagging is further based on the determination of whether the simulatedvehicle reaches the destination location in each of the set of newsimulations.
 3. The method of claim 2, wherein flagging the startinglocation is further based on a comparison a number of new simulationswhere the simulated vehicle does not the destination location to athreshold value.
 4. The method of claim 1, wherein the service areaincludes a boundary across which the simulated vehicle is unable tocross, and wherein determining the simulated vehicle reaches thedestination location in the new simulation is based on whether thesimulated vehicle becomes stranded by the boundary.
 5. The method ofclaim 1, wherein the starting location is identified by randomlyselecting a location within the service area.
 6. The method of claim 1,wherein the starting location is identified from a plurality ofpredetermined pickup and drop off locations within the service area. 7.The method of claim 6, wherein the starting location is identified fromthe plurality of predetermined pickup and drop off locations randomly.8. The method of claim 6, wherein the starting location is identifiedfrom the plurality of predetermined pickup and drop off locations inorder to test a specific area within the service area.
 9. The method ofclaim 1, wherein the destination location is identified from a pluralityof predetermined pickup and drop off locations within the service area.10. The method of claim 1, wherein the simulation is run using a routingsystem software stack for an autonomous vehicle to determine thedetermined route.
 11. The method of claim 1, wherein the particular typeof maneuver is changing lanes.
 12. The method of claim 1, furthercomprising: flagging a plurality of problematic areas; and drawing apolygon around the plurality of problematic areas and the startinglocation.
 13. A system for identifying problematic areas within aservice area for an autonomous vehicle transportation service, thesystem comprising: one or more server computing devices having one ormore processors configured to: identify a starting location within theservice area corresponding to a potential pickup location for passengersor cargo for the service; identify a destination location within theservice area for the starting location; run a simulation to determine aroute for a simulated vehicle to travel between the starting locationand the destination location; determine that the determined routeincludes a particular type of maneuver; run a new simulation withoutallowing the simulated vehicle to complete the particular type ofmaneuver; determine whether the simulated vehicle reaches thedestination location in the new simulation; and based on thedetermination of whether the simulated vehicle reaches the destinationlocation in the new simulation, flag at least one of the startinglocation or destination location as potentially problematic areas. 14.The system of claim 13, wherein the one or more processors are furtherconfigured to: run a set of simulations for the starting location byidentifying a plurality of destination locations for the startinglocation and determining routes between the starting location and eachof the plurality of destination locations; identify ones of thedetermined routes that include the particular type of maneuver; run aset of new simulations without allowing the simulated vehicle tocomplete the particular type of maneuver; and determine whether thesimulated vehicle reaches the destination location in each of the set ofnew simulations, and wherein flagging is further based on thedetermination of whether the simulated vehicle reaches the destinationlocation in each of the set of new simulations.
 15. The system of claim13, wherein the service area includes a boundary across which thesimulated vehicle is unable to cross, and wherein determining thesimulated vehicle reaches the destination location in the new simulationis based on whether the simulated vehicle becomes stranded by theboundary.
 16. The system of claim 13, wherein the starting location isidentified by randomly selecting a location within the service area. 17.The system of claim 13, wherein the starting location is identified froma plurality of predetermined pickup and drop off locations within theservice area.
 18. The system of claim 13, wherein the destinationlocation is identified from a plurality of predetermined pickup and dropoff locations within the service area.
 19. The system of claim 13,wherein the simulation is run using a routing system software stack foran autonomous vehicle to determine the determined route.
 20. The systemof claim 13, wherein the one or more processors are further configuredto: flag a plurality of problematic areas; and draw a polygon around theplurality of problematic areas and the starting location.